Abstract
This paper examines the application of deep learning in autonomous driving vehicles. The rapid development of autonomous driving technology has provided vast possibilities for utilizing deep learning algorithms. Firstly, this paper introduces the technical components and fundamental knowledge of autonomous driving vehicles. It then delves into the basic principles of deep learning in autonomous driving and its specific applications in various aspects, including visual perception, perception and decision-making, and control and execution. Furthermore, challenges faced by deep learning in autonomous driving are discussed, and future development directions are anticipated. Through this study, a better understanding of the current application status and future trends of deep learning technology in the field of autonomous driving can be obtained.
Reference8 articles.
1. Zang D, Wei Z, Bao M, et al. Deep learning–based traffic sign recognition for unmanned autonomous vehicles[J]. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2018, 232(5): 497-505.
2. Schneider L, Hafner M, Franke U. The Stixel world–A comprehensive representation of traffic scenes for autonomous driving[J]. at - Automatisierungstechnik, 2018, 66(9): 745-751.
3. Lateef F, Ruichek Y. Survey on semantic segmentation using deep learning techniques[J]. Neurocomputing, 2019, 338-348.
4. Pavithra G, Dhanya N. Curve Path Prediction and Vehicle Detection in Lane Roads Using Deep Learning for Autonomous Vehicles[J]. International Journal of Recent Technology and Engineering (IJRTE), 2019, 7(5s3): 167-170.
5. Yudin A D, Skrynnik A, Krishtopik A, et al. Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection[J]. Optical Memory and Neural Networks, 2019, 28(4): 283-295.